go-gene-matrix takes a GO vs. GO distance matrix and a gene-go-mapping file

and makes a gene vs. go distance matrix based on the shortest distances found
between each gene and go term.

scripts/geneontology/go-gene-matrix

@@ -0,0 +1,80 @@
+#!/usr/bin/python
+
+import os, sys
+import getopt
+sys.path.append('../..')
+from fluents import dataset
+import numpy
+
+max_val = numpy.inf
+no_nan = False
+
+def print_help():
+    print
+    print "Usage: go-gene-matrix <go-dist-matrix.ftsv> <gene-go-mapping.txt>"
+    print
+    print "Description:"
+    print "    Takes a GO term by GO term distance matrix and a file that"
+    print "    maps GO terms to genes as input arguments and produces a"
+    print "    dataset that contains the shortest distances between all"
+    print "    genes and GO terms."
+    print
+    print "Options:"
+    print "    -h, --help       Show this help text."
+    print "    -m, --max-dist   Trunkate all distances to this value."
+    print
+
+def get_parameters():
+    global max_val
+    short_opts = "hm:"
+    long_opts = ["help", "max-dist="]
+
+    options, params = getopt.getopt(sys.argv[1:], short_opts, long_opts)
+    for opt, val in options:
+        if opt in ['-h', '--help']:
+            print_help()
+            sys.exit(0)
+        elif opt in ['-m', '--max-dist']:
+            max_val = int(val)
+
+    if len(params) < 2:
+        print_help()
+        sys.exit(1)
+
+    return params
+
+if __name__ == '__main__':
+    params = get_parameters()
+
+    # Read dataset
+    fd = open(params[0])
+    ds = dataset.read_ftsv(fd)
+    array = ds.asarray()
+    fd.close()
+
+    # Read mapping
+    sorted_keys = []
+    mapping = {}
+    fd = open(params[1])
+    lines = fd.readlines()
+    for line in lines:
+        values = line.split()
+        if len(values) > 0:
+            mapping[values[0]] = values[1:]
+            sorted_keys.append(values[0])
+
+    # Create new dataset
+    matrix = numpy.zeros((len(sorted_keys), ds.shape[0]))
+    dim = ds.get_dim_name(0)
+    for i, gene in enumerate(sorted_keys):
+        for j, go in enumerate(ds[dim]):
+            min = max_val
+            for go2 in mapping[gene]:
+                if ds[dim].has_key(go2) and array[j, ds[dim][go2]] < min:
+                    min = array[j, ds[dim][go2]]
+            matrix[i, j] = min
+    out_ds = dataset.Dataset(matrix, 
+                             (('genes', sorted_keys), ('go-terms', ds[dim])),
+                             "Gene by GO matrix")
+    dataset.write_ftsv(sys.stdout, out_ds)
+